{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import numpy as np\r\n",
    "\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "%matplotlib inline"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "#引入 支持向量机 Calssify\r\n",
    "from sklearn.svm import SVC\r\n",
    "\r\n",
    "#引入数据集\r\n",
    "from sklearn import datasets"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "iris = datasets.load_iris()\r\n",
    "\r\n",
    "x = iris['data'][:,(2,3)]\r\n",
    "y = iris['target']\r\n",
    "\r\n",
    "setosa_or_versicolor = (y==0) | (y==1)\r\n",
    "\r\n",
    "x = x[setosa_or_versicolor]\r\n",
    "y = y[setosa_or_versicolor]\r\n",
    "\r\n",
    "#建立支持向量机\r\n",
    "svm_clf = SVC(kernel='linear',C=float('inf'))\r\n",
    "svm_clf.fit(x,y)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "SVC(C=inf, kernel='linear')"
      ],
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=inf, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=inf, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "x0 = np.linspace(0,5.5,200)\r\n",
    "pred_1 = 5*x0 - 20\r\n",
    "pred_2 = x0 - 1.8\r\n",
    "pred_3 = 0.1*x0 + 0.5\r\n",
    "\r\n",
    "def plot_svc_bd(svm_clf,start,end,sv = True):\r\n",
    "    w = svm_clf.coef_[0]\r\n",
    "    b = svm_clf.intercept_[0]\r\n",
    "\r\n",
    "    x0 = np.linspace(start,end ,200)\r\n",
    "    #决策边界绘制\r\n",
    "    decision_bd = -w[0]/w[1]* x0 - b/w[1]\r\n",
    "\r\n",
    "    margin = 1/w[1]\r\n",
    "\r\n",
    "    # 上界\r\n",
    "    up = decision_bd + margin\r\n",
    "    #下界\r\n",
    "    down = decision_bd - margin\r\n",
    "\r\n",
    "    plt.plot(x0,decision_bd,'k-',linewidth = 2)\r\n",
    "    plt.plot(x0,up,'k--',linewidth = 2)\r\n",
    "    plt.plot(x0,down,'k--',linewidth = 2)\r\n",
    "\r\n",
    "    if(sv == True):\r\n",
    "        svs = svm_clf.support_vectors_\r\n",
    "        plt.scatter(svs[:,0],svs[:,1],s = 180,facecolors = '#FAA')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "plt.figure(figsize = (14,4))\r\n",
    "plt.subplot(121)\r\n",
    "\r\n",
    "# iris 数据集\r\n",
    "plt.plot(x[:,0][y==1],x[:,1][y==1],'bs')\r\n",
    "plt.plot(x[:,0][y==0],x[:,1][y==0],'ys')\r\n",
    "\r\n",
    "# 1.分割边界\r\n",
    "# plt.plot(x0,pred_1,'g--',linewidth = 2)\r\n",
    "plt.plot(x0,pred_2,'m--',linewidth = 2)\r\n",
    "plt.plot(x0,pred_3,'r--',linewidth = 2)\r\n",
    "\r\n",
    "plt.axis([0,5.5,0,2])\r\n",
    "\r\n",
    "plt.plot(x0,pred_2,'m--',linewidth = 2)\r\n",
    "plt.plot(x0,pred_3,'r--',linewidth = 2)\r\n",
    "\r\n",
    "\r\n",
    "#2.决策边界\r\n",
    "plt.subplot(122)\r\n",
    "plt.plot(x[:,0][y==1],x[:,1][y==1],'bs')\r\n",
    "plt.plot(x[:,0][y==0],x[:,1][y==0],'ys')\r\n",
    "plot_svc_bd(svm_clf,0,5.5)\r\n",
    "\r\n",
    "plt.axis([0,5.5,0,2])\r\n",
    "\r\n",
    "\r\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(0.0, 5.5, 0.0, 2.0)"
      ]
     },
     "metadata": {},
     "execution_count": 5
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "a6dc62afd8b03c17538a9dfce2fcb18f62cec380cc7b77050462a64b7e4e4814"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.8.0 32-bit"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}